Metabolically Healthy Obesity and Risk of Incident Chronic Kidney Disease in a Korean Cohort Study.

Background: The incident of chronic kidney disease (CKD) of metabolically healthy obesity (MHO) has not been consistently determined. Methods: This study used data of Anseong Ansan community-based cohort, a part of the Korean Genome and Epidemiology Study (KoGES) provided by the Korea Center for Disease Control and Prevention (KCDC). Surveys were received from the Anseung and Ansan residents every two years between 2001–2002 and 2015–2016 for a total of 7 surveys over all. The subjects were divided into 4 phenotypes based on the presenting obesity and metabolic syndrome; 1) metabolically healthy normal weight (MHNW), 2) metabolically healthy obesity (MHO), 3) metabolically abnormal normal weight (MANW), and 4) metabolically abnormal obesity (MAO). Data were analyzed using the Cox proportional hazards regression model. Results: Of 8,865 subjects, 1,551 cases of 49,995 person-year (3.1%) developed incident CKD. At an adjusted hazard ratio (HR) of 1.13, the MHO group was not associated with a higher risk of incident CKD (95% confidence interval (CI): 0.92–1.41, P =0.234, using MHNW as the reference). The adjusted HRs of the MANW and MAO groups for incident CKD were significantly higher than those of the MHNW groups: 1.31 (95% CI: 1.05–1.64, P=0.017) for MANW and 1.49 (95% CI: 1.23–1.79, P<0.001) for MAO. Conclusion: MHO is not associated with a high risk of CKD, and that MANW and MAO increase the risk of the incident CKD. Thus, it is important to consider metabolic health status rather than obesity when evaluating CKD risk.


Introduction
Chronic kidney disease (CKD) is associated with loss of kidney function and other complications. high in this group (5,6). However, whether MHO is a risk factor for CKD has not been elucidated, thus establishing a need to investigate the relationship between MHO and CKD (7). Few studies conducted have not shown consistent results. In a study among 3,136 Japanese people using a 8-year follow-up survey of workplace health screenings, those in the MHO group were more likely to develop CKD than those determined to be metabolically healthy and nonobese (odds ratio (OR): 0.83, 95% confidence interval (CI): 0.36-1.72, P = 0.64) (8). A crosssectional study on 2,324 Chinese people found that the MHO group did not have a higher risk of CKD (OR: 0.79, 95% CI: 0.29-2.14, P=0.64) (9). Consistent with the result of that Chinese study was the outcome of the Tehran Lipid Glucose Study in Iranian study (2015) (hazard ratio (HR): 1.23, 95% CI: 0.93-1.62) (10). In a Korean study, in the MHO group 1.38 times more likely to develop CKD than the MHNW group (HR: 1.38, 95% CI: 1.01-1.87) (11). In addition, these existing studies have multiple limitations: short follow-up periods that not is sufficient to elucidate the risk of CKD (8), cross-sectional study designs that are unable to identify cause-andeffect relationships (9), and a lack of reflection on the effects of lifestyle choices like exercising, smoking, and drinking (8). The general population was not represented (11). This study was designed to overcome these all limitations. The aim of this study was to determine the longterm relationship between MHO and CKD. This study was designed to specifically address the limitations of previous studies by using 14 years of long-term follow-up data; quantitatively measuring lifestyle habits like exercising, smoking, and drinking; and using data that represents the general adult population of Korea.

Data and study population
This study used data of Anseong Ansan community-based cohort, a part of the Korean Genome and Epidemiology Study (KoGES) provided by the Korea Center for Disease Control and Prevention (KCDC). Surveys were received from the Anseung and Ansan residents every two years between 2001-2002 and 2015-2016 for a total of 7 surveys over all. The data of the 10,030 subjects (5,018 from Anseung and 5,012 from Ansan) were reviewed originally for analysis and those meeting the following conditions are excluded: 1) GFR <60 ml / min per 1.73 m 2 (equivalent to 3-5 levels of CKD) (CKD baseline n=226 plus proteinuria n=251); 2) history of cancer (n= 82); 3) kidney disease and/or urinary tract infection (n= 326); and 4) missing data at baseline survey (n=280). In the end, 8,865 participants were included in the analysis.

Ethical approval
Institutional Review Board at the Korea Center for Disease Control and Prevention (KCDC) and Jeonbuk National University

Definition of metabolic health and obesity states
Metabolic syndrome was defined by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP) (13). Obesity in this study was defined using the Asian standard for BMI, which is 25 kg/m 2 or more (11). Subjects were divided into phenotypes according to the combination of the presence or absence of obesity and the presence or absence of metabolic syndrome: 1) MHNW;(2) MHO;(3) metabolic abnormal, normal weight (MANW); and (4) metabolic abnormal, obesity (MAO).

Clinical and laboratory measurements
For both systolic and diastolic blood pressure, three measurements were taken and the mean was used. The cohort provided data on total alcohol consumption (g/d), total tobacco use (pack/yr), and total physical activity (metabolic equivalent of task, MET/wk).

Statistical analysis
SPSS 20.0 (Chicago, IL, USA) and R programming (for calculating GFR) were used for data processing and analysis. The life table analysis that shows the curve for cumulative survival free from incident CKD of 4 phenotypes of obesity was used. The hazard ratio was calculated using the Cox proportional hazards regression model to compare the risk of CKD according to the 4 groups. MHNW was set as the reference group; the hazard ratio and the 95% CI of MHO, MANW and MAO were obtained to determine the statistical significance. Three models were run after adjusting for variables: age, sex, and income in Model 1, plus baseline GFR, drinking, smoking, physical activity, and history of cardiovascular disease for Model 2, plus ALT, AST, uric acid, CRP, GTP, and systolic blood pressure for Model 3.

Results
Of the participants, 70.6% (n=6,256) were metabolically healthy and, of these, 42.3% (n=3,747) were obese. The MHO group accounted for 21.5% (1,902) of the total subjects and 50.8% of the obese population. Compared to the MHNW subjects, those in the MHO group were more likely to be male, have a higher income and engage in physical activity less. During the 14-year followup period, 49,995 person-years, which is the number of follow-up times multiplied by the number of people, were determined, and 1,551 cases of the person-year (3.1%) developed incident CKD ( Table 1).
The crude incidence rate was 2.5% (615/24771.5 person-year) in the MHNW group, 2.4% (266/11,138 person-year) in the MHO, 5.1% (205/4,036.5 person-year) in the MANW group, and 4.5% (465/10,049 person-year) in the MAO group. The curve for cumulative survival free from incident CKD is presented in Fig. 1. Those in the MANW and MAO groups had a higher probability of developing incident CKD than those in the MHNW and MHO groups, but within these groups, there were no significant differences between MHNW and MHO or MANW and MAO individuals (log-rank test, P =0.642, P =0.299, respectively). Table 2 shows the associations of obesity categories (non-obese, obese), BMI (under, normal, over, obese), NECA ATP-III components and numbers, and the results of running Model 1, Model 2, and Model 3. The adjusted hazard ratio (HR) of obese individuals for incident CKD (using non-obese subjects as the reference) was 1.22 (95% CI: 1.06-1.42, P=0.006, Model 3). The adjusted hazard ratio (HR) of underweight individuals was 0.48 (95% CI: 0.25-0.91, P=0.023, Model 3), and the adjusted HR of obese individuals for incident CKD was 1.24 (95% CI: 1.03-1.48, P=0.021, Model 3, with normal weight used as the reference). All ATP-III components were associated with incident CKD in Model 1 and Model 2, with HRs ranging from 1.14 to 1.44. In Model 3, the adjusted HR was 1.33 (95% CI: 1.15-1.54, P<0.001) for high triglyceride levels and 1.36 (95% CI: 1.14-1.63, P=0.001) for high blood plasma glucose, respectively. As the number of metabolic syndrome components increased from 2 to 5, the risk of CKD increased proportionally from 1.38 (95% CI: 1.14-1.   (Table 2).

Discussion
The results of this study showed that MHO was not a risk for incident CKD, which was consistent with the results of prospective cohort studies in the people of Japan (8) (16). Those factors strongly support the idea that systematic inflammation is independently involved in CKD in addition to the effect of metabolic health status irrespective of obesity. As shown by the significantly higher HRs of obese groups reported in Table 2, this study supports other studies indicating obesity as a risk factor for the onset and progression of CKD when metabolic health was not considered (17). Increased visceral adiposity, fatty acids, cytokines, and adipokines may cause a decline in kidney function, leading to the development of hypertension (18), and obesity itself could be harmful to renal function (19). These results suggest that metabolic syndrome is a bigger risk factor than weight for the development of CKD (Fig. 1). In one meta-analysis (20), the risk of CKD was much higher in metabolically unhealthy groups regardless of obesity condition; risk ratio was  (20), waist circumstance (WC) and waist-toheight ratio (WHtR) were more closely related to CKD than BMI.  (25). Past research has shown that elevated metabolized triglyceride-rich apoB-containing lipoproteins may promote the progression of renal insufficiency (26). Therefore, individuals who have high triglyceride and blood sugar levels should be carefully monitored to prevent CKD and educated to manage their triglycerides and blood sugar even if they are not diagnosed with metabolic syndrome. It is also necessary to study how hyperlipidemia and hyperglycemia increase the incidence of CKD. Among the components of metabolic syndrome, hypertension, abdominal obesity, and low HDL cholesterol are associated with CKD, and their mechanism has been confirmed in meta-analysis and other studies (24,27). However, in this study, hypertension, lower HDL cholesterol and abdominal obesity were not related to CKD development, suggesting the need for further investigation. This study has some limitations. First, GFR values were estimated using the CKD-EPI formula based on creatinine rather than actual GFR measurements, which could overestimate or underestimate the GFR. Second, 4 different types of obesity and metabolic health state were classified based on baseline in the first year, so the classifications could be shifted throughout 14 years of follow-ups. Third, this study did not use central obesity such as WC or WHtR, which is more representative of adipose tissue distribution and more associated with CKD. Future studies should address these limitations. This study also had several strengths. First, this can be considered as a representative study of Koreans using data sampled in accordance with a standardized process in the country and followed by 14 years of follow-up. Lifestyle was more precisely and accurately reflected in the analysis by using total alcohol consumption (g / d), smoking (pack / yr) and physical activity (MET/wk) instead of simply using frequency or yes / no questions to determine usage or engagement. Finally, the KoGES surveys provided detailed information on laboratory tests, were carefully stand-ardized, and maintained a high quality of procedures (e.g., 3 measurements of blood pressure).

Conclusion
MHO was not associated with a high risk of CKD and that being MANW or MAO increases the incidence of CKD. The status of metabolic health is associated more with the development of CKD than obesity. The results of this study can be used as basic data to develop programs for metabolic syndrome management, obesity prevention, and CKD prevention, and to establish governmental health policies for public health centers and clinics in Korea.

Ethical considerations
Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors.